Guardrails in large language models are mechanisms or strategies used to ensure that the outputs of these models are aligned with ethical, safety, and quality standards. They help prevent harmful, biased, or nonsensical outputs during inference.
Common guardrails include content filtering (to block inappropriate or unsafe outputs), fine-tuning (to align the model with specific behaviors), and reinforcement learning with human feedback (RLHF) to improve the model’s adherence to desired guidelines.
Additionally, developers implement input validation, prompt engineering, and monitoring systems to detect and mitigate potential issues. Guardrails are essential for ensuring large language models are reliable and trustworthy, particularly in high-stakes applications like healthcare, education, or customer support.